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LibraxisAI/
whisper-large-v3-turbo-mlx-q8

Name: whisper-large-v3-turbo-mlx-q8
Availability: InStock

automatic-speech-recognition

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whisper-large-v3-turbo-mlx-q8

whisper-large-v3-turbo-mlx-q8 is an MLX-ready Whisper speech-to-text checkpoint derived from openai/whisper-large-v3-turbo for local transcription on Apple Silicon.

Intended use

Local speech-to-text transcription on Apple Silicon
Batch or interactive audio transcription experiments
Multilingual ASR workflows when supported by the upstream Whisper checkpoint

Out of scope

Safety-critical decisions without domain expert review
Claims of benchmark superiority not backed by published evaluation data
Non-MLX runtime guarantees; this card documents the shipped HF checkpoint, not every possible serving stack
Speaker diarization, clinical interpretation, or audio enhancement

Training and conversion metadata

Parameter	Value
Repository	`LibraxisAI/whisper-large-v3-turbo-mlx-q8`
Base model	`openai/whisper-large-v3-turbo`
Task	`automatic-speech-recognition`
Library	`transformers`
Format	MLX / Apple Silicon checkpoint
Quantization	Q8
Architecture	Not declared in config
Model files	2
Config model_type	`whisper`

This card only reports metadata present in the Hugging Face repository, existing card frontmatter, or public config files. Missing benchmark, dataset, or training-run details are left explicit rather than reconstructed.

Tested inference path

Inference for this checkpoint has been tested with LibraxisAI/mlx-batch-server.
This is the recommended tested path for operator-controlled local inference on Apple Silicon.

Aspect	Status
Tested runtime	`LibraxisAI/mlx-batch-server`
Target hardware	Apple Silicon
Inference mode	Local / self-hosted
Hugging Face Hosted Inference	Disabled for this repository (`inference: false`)

This does not claim compatibility with every possible serving stack. It documents the path that has been exercised for this published checkpoint.

Usage

Python

python

import mlx_whisper

result = mlx_whisper.transcribe(
    "audio.wav",
    path_or_hf_repo="LibraxisAI/whisper-large-v3-turbo-mlx-q8",
)
print(result["text"])

Notes

Use local audio files supported by mlx_whisper.
For long recordings, split audio into manageable chunks before transcription.

Example output

No public sample output is currently declared for this checkpoint.

Quantization notes

Aspect	Original/base checkpoint	This checkpoint
Lineage	`openai/whisper-large-v3-turbo`	`LibraxisAI/whisper-large-v3-turbo-mlx-q8`
Runtime target	Upstream runtime format	MLX on Apple Silicon
Quantization	Base precision or upstream-declared format	Q8
Published quality delta	Not declared in public metadata	Not declared in public metadata

Limitations

No public benchmarks for this checkpoint are declared in the model metadata.
No public benchmark claims are made by this card unless listed in the frontmatter.
Validate outputs on your own domain data before relying on this checkpoint.
Memory use and speed depend heavily on Apple Silicon generation, unified-memory size, audio duration, and language complexity.

License

mit. Check the upstream/base model license as well when a base model is declared.

Citation

bibtex

@misc{libraxisai-whisper-large-v3-turbo-mlx-q8,
  title = {whisper-large-v3-turbo-mlx-q8},
  author = {LibraxisAI},
  year = {2026},
  howpublished = {\url{https://huggingface.co/LibraxisAI/whisper-large-v3-turbo-mlx-q8}},
  note = {MLX checkpoint published by LibraxisAI}
}